Welding Process Parameters Research Articles

Multiobjective optimization of laser welding parameters for P92 steel based on MFO/MOEAD-Kriging

P92 steel is used in steam equipment of thermal power plants and nuclear power plants. It needs to withstand high temperature and high pressure during service, so high-quality welds are required. Laser welding can easily obtain good weld quality and reduce the probability of coarse grains and welding defects in the heat-affected zone. Post-weld deformation, residual stress, and weld hardness are important indexes for evaluating weld quality, which largely depends on the combination of laser welding process parameters. The multiobjective optimization methods of moth-flame optimization (MFO) and multiobjective evolutionary algorithm based on decomposition under the Kriging model were compared in this research. It was found that the MFO algorithm has a faster optimization speed and better overall effect. The Optimal Latin hypercube sampling method was used to design the simulation test. The welding seam-forming process was simulated by the SYSWELD software. The Kriging model was used to make the nonlinear relationship between the process parameters and the weld quality indexes significant. A new data set was designed to evaluate the accuracy of the model. On this basis, the efficiency of the combination of process parameters optimized by the two algorithms was compared. The simulation and experimental results were in good agreement with the multiobjective optimization results of the MFO. In the experimental verification, the optimized weld quality was analyzed from the aspects of microstructure and mechanical properties. The results showed that the multiobjective optimization method quickly and effectively reduces the probability of weld quality defects in actual welding, thereby improving weld quality. This multiobjective optimization method provides valuable research ideas for engineering research and development to effectively shorten its development cycle.

Optimization and GRA prediction of Al–Cu pulsed laser welding process based on RSM

In this paper, the process parameters of pulsed laser welding of Al–Cu are studied using response surface methodology (RSM) and grey relation analysis (GRA) to propose optimal directions for lap-shear strength of the joints. A single-factor experiment was conducted to find the suitable process parameter windows. The RSM models were established and analyzed with laser power, welding speed, pulse width, and frequency as inputs and joint lap shear, interfacial weld width, and weld-penetration-depth as outputs. With the use of GRA of interfacial weld width and weld penetration depth with the lap-shear force of joints, the improved direction for the lap-shear force of joints can be proposed.

Investigation of The Weldability of PLA Plus Sheets with Different Infill Ratios by Friction Stir Welding

Although the joining processes of plastics are typically carried out through welding, adhesive bonding, or mechanical fastening elements, the production of complex and large parts often requires welding technology. In this study, the effect of part infill ratio (20%, 40%, 60%, 80%, and 100%) on the welding strength of 3D printed PLA Plus parts was evaluated through friction stir welding (FSW). Tensile tests and temperature measurements were carried out to examine the effects of friction stir welding process parameters (feed rate: 50 and 100 mm/min and rotational speed: 1000 and 1500 rpm) on the structure and mechanical properties of friction stir welding. Moreover, visual inspections were performed to detect defects in the weld zone. Compared to the PLA Plus samples given as reference according to the infill ratios, the highest welding strengths were obtained at 80%, 60% and 100% infill ratios (29.4 MPa, 17.47 MPa and 41.12 MPa and 112.38%, 97.48%, 87.04% efficiency, respectively). As a result, it was determined that at low infill ratios (20% and 40%), the weld quality was negatively affected, and a surface tunnel defect occurred in the weld zone. It has been determined that the weld quality in FSW is significantly affected by the temperature occurring during the process. The study has shown that parts printed at different infill ratios, especially on a 3D printer, can be combined with friction stir welding and that the efficiency of the welding process can be increased by optimizing the infill ratios.

Laser Beam Welding Parametric Optimization for AZ31B and 6061-T6 Alloys: Residual Stress and Temperature Analysis Using a CCD, GA and ANN

Laser beam welding (LBW) is a technology that efficiently joins materials using a concentrated laser beam, providing advantages such as a small heat-affected zone (HAZ), high precision, adaptability to various materials, and seamless integration into automated processes. Precision in LBW is limited by challenges in selecting appropriate process parameters, performing expensive and risky physical experiments, and dealing with materials with high thermal conductivity and low melting temperatures, such as AZ31B and AA6061. This study aims to predict laser welding process parameters, encompassing beam power (4000W-6000W), spot size (0.4mm-0.8mm), welding speed (30mm/s-50mm/s), and segment number (5-15) utilizing the Response Surface Methodology (RSM), Genetic Algorithm (GA) and backpropagation neural networks. Beam power significantly affects peak temperature, leading to deeper laser penetration and a higher maximum temperature, while the segment number plays a crucial role in residual stress by potentially causing uneven cooling and distinct thermal gradients. The ANN results confirm the model's accuracy and the GA-predicted process parameters include a laser power of 4000W, welding speed of 50mm/s, spot size of 0.6mm, segment number of 15, peak temperature range of 888.9°C to 1020.5°C, and a preferred equivalent stress of 1396.62 MPa.

Intelligent optimization of non-rigidly supported stationary shoulder friction stir welding parameters

During the online welding of railway aluminum alloy trucks using fixed shoulder stirring friction welding technology, defects such as flying edges, cracks, and tunnels may occasionally occur due to poor coordination of the welding process parameters. This inconsistency can compromise the success rate of the welding. To enhance the quality and efficiency of the welding repairs, an optimization of the welding process parameters of the developed equipment was undertaken. An intelligent prediction model for welding parameters was established based on the BSO-BP neural network. The model was trained with 17 sets of authentic welding data, and its accuracy was validated through a comparison of predictive outputs with actual welding outcomes. The BSO-BP neural network demonstrated a maximum error of 1.85% and a relative error of 1.27%, showing a marked improvement over traditional BP neural network predictions. Utilize the optimized welding process parameters obtained from a trained predictive model to conduct welding experiments under actual working conditions. During the experiment, the deformation of the test plates was minimal. The resulting welds were smooth, free of defects such as flash and grooves, and the overall welding quality was good. The tensile strength of the welds, at 262.95 MPa, was close to the predicted results and reached 78.49% of the base material’s tensile strength. In the microstructure of the weld, sporadically distributed dimples of varying sizes can be observed, indicating the presence of second phase particles which contribute to enhancing the tensile strength of the weld, thereby meeting the strength requirements of the aluminum alloy car body. The parameter optimization method used has certain reference value and can provide guidance for similar research.

Effect of Welding Process Parameters on the Mechanical Properties of TIG and MIG Welds in HSS X65 Pipe-A Review

The study focused on the importance of the different welding parameters on the mechanical behavior of High Strength Steel (HSS) X65 steel pipes by reviewing the advantages of parameter optimization for the Tungsten Inert Gas (TIG) - Metal Inert Gas (MIG) welding processes. The parameters considered in the study include welding speed, welding current, welding voltage and gas flowrate of the welding. The effects of improper selection and parameter optimizations were highlighted and illustrated using different metallurgical and mechanical instances. The outcome of the study indicates that adequate parameter optimization aids in obtaining good weld quality with adequate mechanical and microstructural properties. Furthermore, it helps in the determination of variation in hardness in the heat affected zone as well as the base metal. Thus, this study provides insight to welding engineers on the importance of parameter optimization in the welding of steel pipe.

Multi-objective optimization of laser welding process parameters of steel/Al based on BO-RF and MOJS

Multi-objective optimization of laser welding process parameters of steel/Al based on BO-RF and MOJS

Development of FSW Process Parameters for Lap Joints Made of Thin 7075 Aluminum Alloy Sheets.

The article describes machine learning using artificial neural networks (ANNs) to develop the parameters of the friction stir welding (FSW) process for three types of aluminum joints (EN AW 7075). The ANNs were built using a total of 608 experimental data. Two types of networks were built. The first one was used to classify good/bad joints with MLP 7-19-2 topology (one input layer with 7 neurons, one hidden layer with 19 neurons, and one output layer with 2 neurons), and the second one was used to regress the tensile load-bearing capacity with MLP 7-19-1 topology (one input layer with 7 neurons, one hidden layer with 19 neurons, and one output layer with 1 neuron). FSW parameters, such as rotational speed, welding speed, and joint and tool geometry, were used as input data for ANN training. The quality of the FSW joint was assessed in terms of microstructure and mechanical properties based on a case study. The usefulness of both trained neural networks has been demonstrated. The quality of the validation set for the regression network was approximately 93.6%, while the errors for the confusion matrix of the test set never exceeded 6%. Only 184 epochs were needed to train the regression network. The quality of the validation set was approximately 87.1%. Predictive maps were developed and presented in the work, allowing for the selection of optimal parameters of the FSW process for three types of joints.

Experimental Modeling and Multi-Response Optimization in Friction Stir Welding Process Parameters of AA2024-T3 Using Response Surface Methodology and Desirability Approach

Experimental Modeling and Multi-Response Optimization in Friction Stir Welding Process Parameters of AA2024-T3 Using Response Surface Methodology and Desirability Approach

Process Parameters of High Frequency Welding.

High frequency (HF) welding of polymer materials is increasingly used in modern manufacturing processes. The literature on HF welding process parameters was reviewed and it was found that 3-5 basic welding parameters were considered, which is insufficient for the scientific study of HF welding of polymeric materials. This article presents the mathematical expressions for the evaluation combining 17 influential parameters. For the first time, the specific and latent heat of the welded polymer material were used. The breaking forces of welds made by RF welding are investigated by varying the anode current, the coupling capacitor, and the exposure time of the HF electromagnetic fields. It was found that the amount of HF energy supplied depends on the breaking forces of the weld. A characteristic inflection point was also observed in the graph of the dependence of the breaking forces on the percentage of the coupling capacitor and the anode current. During elongation, it was observed that the weld is separated by peeling before the inflection point and breaks after the inflection point by tearing at the extruded edges of the weld. If the HF energy is applied to the weld for too long, there will be excessive melting of the material in the weld, thinning of the weld, unfavourable appearance of the extruded edges and electrical breakdown, and a drastic drop in the breaking force.

Intelligent Prediction of FSW Physical Quantity and Joint Mechanical Properties

Friction stir welding (FSW) process parameters influence welding temperature field and axial force, which affect welding strength. At present, how the FSW process parameters of aluminum alloy 2219-T8 thick plates influence process physical quantity and how the process physical quantity changes the tensile strength about the welded joint are unknown. We focus on the intelligent prediction of FSW temperature, axial force, and mechanical properties, to provide a basis for FSW process control of aluminum alloy 2219-T8 thick plate. Firstly, we conducted the FSW experiment of aluminum alloy 2219-T8 thick plate. Then, we input the welding process parameters, set up a prediction model by particle swarm optimization-back propagation (PSO-BP) neural network to predict the peak temperature and axial force. Finally, we input the peak temperature and axial force, use genetic algorithm-back propagation (GA-BP) neural network to establish a weld tensile strength estimation model, and comply with the prediction of tensile strength.

Evaluating Welding Electrode Angle and Root Gap Effects on Joint Quality

Welding is a type of manufacturing method that offers greater advantages than riveting for combining metals or sheets. By adjusting the welding process parameters to predict welding strength, a higher quality of joint can be achieved. In this study, the weld processing parameters welding current (WC), electrode angle (EA), and root gap (RG) were taken into account. We performed destructive testing, such as tensile and hardness tests, to evaluate the metal piece's weld strength after it had been joined. Stainless steels and carbon steels, two unrelated materials, were used in this project. The Taguchi method was used to carry out joint processes in arc welding (AW), and nine samples were produced by altering the input parameters. Steel alloy 308L has been used as the electrode for forming connections.

The Role of Nanoparticle Based Coating in Optimizing TIG Welding Parameters for EN31 Steel

This study investigates the optimization of process parameters in TIG welding of EN31 steel alloys, incorporating nano-enhanced materials to improve performance in sustainable manufacturing and energy-efficient applications. The three key input variables considered are welding current (CT), welding speed (WD), and gas flow rate (GF), while tensile strength (TS) and hardness (HS) are taken as output responses. The experiment was designed using the Central Composite Design (CCD) under the Response Surface Methodology (RSM) framework, with 20 experiments conducted. Nanoparticle (TiO2 & B4C) were coated with EN31 steel to enhance chemical reactions, improve weld quality, and promote energy efficiency. Results indicate that the optimal conditions for tensile strength (TS) were achieved at 120 Amps CT, 70 mm/min WD, and 11 lpm GF. For hardness (HS), the optimal conditions were 100 Amps CT, 70 mm/min WD, and 11 lpm GF. Analysis of variance revealed that the welding current (CT) significantly impacts both TS and HS, while the integration of nano materials further improved these mechanical properties by enhancing microstructural refinement and energy efficiency. The experimental data closely aligned with the normal probability plot, confirming the reliability of the results. This study highlights the potential of nano-enhanced TIG welding as a sustainable solution for improving the mechanical properties of steel alloys while promoting energy-efficient manufacturing practices

Hybrid Optimization of Process Parameters in Manual Metal Arc Welding for Nanostructured Hard facing

AISI 310 stainless steel and EN24 steel were welded together using Manual Metal Arc Welding. Minitab and Design Expert software were applied to predict and optimize the results for bead width (BW) and weld penetration (PE). Total of 30 experiments were conducted according to the Box-Behnken design in RSM. Four different welding parameters current, voltage, welding speed, and root gap were employed to examine their effects on output values. The optimal conditions for BW were determined using the SN ratio, leading to an optimal parameter combination of C1-V4-W4-R4. This combination corresponds to a current of 75A, a voltage of 18V, a welding speed of 50 mm/min, and a root gap of 3 degrees. The optimal parameter value obtained from the Design Expert software were used to find the BW and PE to evaluate the weld quality. Under the optimal conditions, the supreme BW achieved was 13.994 mm, while the extreme PE reached 4.43976 mm, indicating satisfactory weld quality.

Optimization of laser welded ASTM A36 mild steel with different laser beam oscillation patterns utilizing experimental and simulation data

Recently, there has been an increase in the use of laser beam welding of mild steel in various industries, including petroleum refineries, power plants, pharmaceuticals, and even residential areas. This research paper focuses on studying the effects of laser welding process parameters, such as laser power and welding speed, on the tensile strength of welds. To do this, three types of laser beam oscillations (sinusoidal path, triangular path, and square path) were performed to weld 125mm x 60 and 1.8 thick sheets of ASTM A36 mild steel alloy. The researchers used statistical tools such as ANOVA to generate mathematical models and experimental designs using the Taguchi method. The results indicate that the optimal welded joint has good mechanical properties after laser welding. For ASTM A36 mild steel, the optimal parameters for laser welding are a laser power of 1800 W, a welding speed of 50 mm/s, and triangular welding mode.

Influence of full penetration welding on the multispectral signal of the process radiation during laser beam welding of austenitic stainless steel

A 100% visual inspection of the welds carried out is not always possible for economic reasons. Different methods of process monitoring are therefore used for non-destructive testing of process and weld seam quality. In the following, the recorded multispectral signals of the reflective process radiation are evaluated using descriptive statistics and characteristics of a full penetration weld during laser beam welding of austenitic stainless steel are worked out. The aim is to detect and localize unintentional full penetration welding at an early stage and thus avoid rejects. For this purpose, adapted sample geometries with varying material thicknesses are prepared for the test series. As a result, the need to vary the welding process parameters to achieve the different welding states (full and partial penetration weld) is avoided. By evaluating the measured values of the wavelength-selective signals, a significant influence on the signal intensity and the signal frequency could be confirmed.

Examination and Optimization of Friction Stir Welding Process Parameters of AA6092 Alloys

Examination and Optimization of Friction Stir Welding Process Parameters of AA6092 Alloys

Numerical calculation and analysis of temperature field in ultrasonic welding of polypropylene dialyzer

Ultrasonic plastic welding is widely used in the bonding process of medical device accessories. In this paper, a thermo-force indirect coupling finite element analysis model was established in the ultrasonic plastic welding process between the blood cap and the shell of polypropylene (PP) dialyzer. The temperature field distribution between the blood cap and the shell was simulated and analyzed by using finite element analysis software, and the influence of welding process parameters on the temperature field was studied. The results show that: by changing the ultrasonic amplitude parameters, welding time parameters, initial pressure, etc., the longer the ultrasonic welding time, the temperature of the welding area will increase. In order to ensure the quality of the dialyzer, it should be controlled within 0.8-1 seconds. The increase of ultrasonic amplitude will make the welding temperature continue to rise, and in order to avoid poor welding, the amplitude should not exceed 120 ?m. The initial pressure has little effect on the temperature field.

Investigation and Multi-Response Optimization of Friction Stir Welding Process Parameters for Stir Cast AA6092/SiC Composite

Investigation and Multi-Response Optimization of Friction Stir Welding Process Parameters for Stir Cast AA6092/SiC Composite

Investigating parametric effects during TIG welding of dissimilar metals

This paper explores the optimization of Tungsten-Inert-Gas (TIG) welding process parameters for creating a hybrid structure of Aluminium 6061 and Stainless Steel 304 using a copper filler rod (ER-Cu). The Welding of these two materials has industrial relevance owing to its weight reduction capabilities and environmental benefits. However, Aluminium and Stainless-Steel have different melting points and thermal properties. Aluminium has twice coefficient of thermal expansion and six times coefficient of thermal conductance as compared to Stainless-Steel. This difference often results in residual stresses and brittle intermetallic compounds in the weld region. We have chosen the Welding Current, Welding Speed, and Gas Flow Rate as input parameters, and Ultimate Tensile Strength (UTS) and Micro-hardness as response parameters. We have employed the Response Surface Methodology (RSM) using a Box-Behnken design to evaluate the influence of input parameters on UTS and Micro-hardness. Furthermore, an Analysis of Variance (ANOVA) is conducted to determine the input parameters' significance on the response parameters. Our surface plots demonstrate that UTS improves with increased Welding Current and reduced Welding Speed. Simultaneously, Micro-hardness increases with elevated Welding Speed and decreased current, up to a specific limit. The peak value of UTS (79 MPa) was observed with a Current range of 85-90 A, Speed range of 95-100 mm/min, and Gas Flow Rate of 14.5-15 l/min. On the other hand, maximum Micro-hardness (260HV) was obtained with a Current range of 80-85 A, Speed range of 105-110 mm/min, and Gas Flow Rate of 14.5-15 l/min. This research contributes to improving the manufacturing process of hybrid structures, specifically by optimizing the advantages of both Aluminium and Stainless Steel while addressing the challenges that arise during their combination. The study's conclusions have major consequences for sectors looking to take advantage on the mutually beneficial characteristics of different metals in welding applications.